/usr/share/pyshared/openopt/kernel/SLE.py is in python-openopt 0.34+svn1146-1build1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | from ooMisc import assignScript
from baseProblem import MatrixProblem
from numpy import asfarray, ones, inf, dot, nan, zeros, any, all, isfinite, eye, vstack, hstack, flatnonzero, isscalar, ndarray, atleast_2d, zeros_like
from numpy.linalg import norm
from oologfcn import OpenOptException
import NLP
from nonOptMisc import scipyInstalled, Vstack
try:
import scipy
scipyInstalled = True
except:
scipyInstalled = False
class SLE(MatrixProblem):
expectedArgs = ['C', 'd']# for FD it should be Cd and x0
probType = 'SLE'
goal = 'solution'
allowedGoals = ['solution']
showGoal = False
FuncDesignerSign = 'C'
solver = 'defaultSLEsolver'
_optionalData = []
_isPrepared = False
def __init__(self, *args, **kwargs):
MatrixProblem.__init__(self, *args, **kwargs)
_useSparse = lambda self: True if (scipyInstalled and self.n > 100) else False
def objFunc(self, x):
if isinstance(self.C, ndarray):
return norm(dot(self.C, x) - self.d, inf)
else:
# TODO: omit code clone in FD ooFun.py, function _D
t1 = self.C_as_csc
t2 = scipy.sparse.csr_matrix(x)
if t2.shape[0] != t1.shape[1]:
if t2.shape[1] == t1.shape[1]:
t2 = t2.T
else:
raise FuncDesignerException('incorrect shape in FuncDesigner function _D(), inform developers about the bug')
rr = t1._mul_sparse_matrix(t2)
return norm(rr.toarray().flatten() - self.d, inf)
def _Prepare(self):
if self._isPrepared: return
self._isPrepared = True
if isinstance(self.d, dict): # FuncDesigner startPoint
self.x0 = self.d
MatrixProblem._Prepare(self)
if self.isFDmodel:
equations = self.C
AsSparse = bool(self.useSparse) if type(self.useSparse) != str else self._useSparse()
C, d = [], []
if len(self._fixedVars) < len(self._freeVars):
Z = dict([(v, zeros_like(self._x0[v]) if v not in self._fixedVars else self._x0[v]) for v in self._x0.keys()])
else:
Z = dict([(v, zeros_like(self._x0[v]) if v in self._freeVars else self._x0[v]) for v in self._x0.keys()])
#Z = self.x0#self._vector2point(zeros(self.n))
for lin_oofun in equations:
if lin_oofun.getOrder(self.freeVars, self.fixedVars) > 1:
raise OpenOptException('SLE constructor requires all equations to be linear')
C.append(self._pointDerivative2array(lin_oofun.D(Z, **self._D_kwargs), useSparse = AsSparse))
d.append(-lin_oofun(Z))
self.d = hstack(d).flatten()
self.C = Vstack(C)
if hasattr(self.C, 'tocsc'): self.C_as_csc = self.C.tocsc()
if isinstance(self.C,ndarray) and self.n > 100 and len(flatnonzero(self.C))/self.C.size < 0.3:
s = "Probably you'd better solve this SLE as sparse"
if not scipyInstalled: s += ' (requires scipy installed)'
self.pWarn(s)
self.x0 = zeros(self.C.shape[1])
# if not self.damp is None and not any(isfinite(self.X)):
# self.X = zeros(self.n)
#ff = lambda x, LLSPprob: LLSPprob.objFunc(x)
#def dff(x, LLSPprob):
# r = dot(LLSPprob.C.T, dot(LLSPprob.C,x) - LLSPprob.d)
# if not LLSPprob.damp is None: r += LLSPprob.damp*(x - LLSPprob.X)
# if LLSPprob.f is not None and all(isfinite(LLSPprob.f)) : r += LLSPprob.f
# return r
#
#def d2ff(x, LLSPprob):
# r = dot(LLSPprob.C.T, LLSPprob.C)
# if not LLSPprob.damp is None: r += LLSPprob.damp*eye(x.size)
# return r
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